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Record W3107264758 · doi:10.1155/2020/6639636

An Incentive Mechanism Model of Credit Behavior of SMEs Based on the Perspective of Credit Default Swaps

2020· article· en· W3107264758 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueComplexity · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsUniversity of Toronto
FundersMajor Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu ProvinceGovernment of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsCredit default swapBusinessCredit referenceCredit default swap indexCredit riskCredit historyCredit enhancementLoanMoral hazardFinanceCredit valuation adjustmentCredit crunchIncentiveCredit card interestFinancial systemEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

The rapid development of credit default swap (CDS) market has changed the manner of credit risk management of banks to some extent and has had a new influence on the bank-enterprise credit model. In this study, the credit financing process of credit risk in small- and medium-sized enterprises (SMEs) gathers within a bank, which makes it difficult for SMEs to raise funds. On the basis of the perspective of CDS, we construct an incentive game model of bank-enterprise credit behavior and analyze the influence mechanism of the credit financing of SMEs on CDS contract coupon rate, CDS payout ratio, bank-enterprise credit effort, and loan recovery rate when considering CDS. The result shows that the CDS contract leads to insufficient supervision after a bank loan, the moral hazard of the SMEs rises, and the probability of credit default events increases. In addition, in view of CDS, the SMEs can access more credit funds.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.102
GPT teacher head0.275
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it